The digitization of X-ray film and automatic detection of weld defects are of great significance for improving the production and processing quality and detection efficiency of large aerospace parts. In some specific scenes, the digital receiver cannot be used for X-ray detection, and the transformation of X-ray film into digital image is the premise of defect recognition. However, it is difficult to realize the high fidelity digitization of X-ray film by existing methods. In this paper, an algorithm of X-ray film defect detection based on depth learning is proposed. Firstly, based on full convolution neural network, the digital image with best exposure time on the X-ray film is automatically selected from the images with different exposure time. Then a defect detection network based on lightweight MoGaA network is designed to detect small target defects in X-ray digital images. The digitization and detection results show that the accuracy of this algorithm for weld defect detection can reach 96%, and good detection effect is obtained.